Search Results for author:
Found 40 papers, 17 papers with code
Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
Conclusions: Machine learning can be applied to predict the risk of rupture for intracranial aneurysms.
Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies
Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma.
Letter to the Editor: What are the legal and ethical considerations of submitting radiology reports to ChatGPT?
This letter critically examines the recent article by Infante et al. assessing the utility of large language models (LLMs) like GPT-4, Perplexity, and Bard in identifying urgent findings in emergency radiology reports.
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis
Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts.
ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessment
To address this, we developed a recurrent transformer model that tracks a surgeon's performance throughout a session by mapping hidden states to six OSATS, derived from kinematic data, using a clinically motivated objective function.
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
Driving Points Prediction For Abdominal Probabilistic Registration
Among the various registration methods proposed for this task, probabilistic displacement registration models estimate displacement distribution for a subset of points by comparing feature vectors of points from the two images.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
A multi-organ point cloud registration algorithm for abdominal CT registration
Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning.
Augmentation based unsupervised domain adaptation
The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellum of the Tc1 mouse model of Down Syndrome – a comprehensive morphometric analysis with active staining contrast-enhanced MRI
Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules.
Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
We introduce $\textit{InExtremIS}$, a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks at the boundary of the objects of interest.
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images.
Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021
The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data.
MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation
In this work, we aim at improving the fetal brain segmentation for SRR in Spina Bifida.
Scale factor point spread function matching: Beyond aliasing in image resampling
Imaging devices exploit the Nyquist-Shannon sampling theorem to avoid both aliasing and redundant oversampling by design.
Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets
We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset.
Scribble-based Domain Adaptation via Co-segmentation
Instead of requiring detailed but time-consuming annotations, scribbles on the target domain are used to perform domain adaptation.
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images.
On the Initialization of Long Short-Term Memory Networks
Weight initialization is important for faster convergence and stability of deep neural networks training.
Knowledge distillation for semi-supervised domain adaptation
As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).
Robust parametric modeling of Alzheimer's disease progression
Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric MRI and PET biomarkers, CSF measurements, as well as cognitive tests.
Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities.
Permutohedral Attention Module for Efficient Non-Local Neural Networks
Medical image processing tasks such as segmentation often require capturing non-local information.
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i. e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method.
Grey matter sublayer thickness estimation in themouse cerebellum
In this work, we introduce a framework to extract the Purkinje layer within the grey matter, enabling the estimation of the thickness of the cerebellar grey matter, the granular layer and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI.
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input.
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification.
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--.
Robust training of recurrent neural networks to handle missing data for disease progression modeling
This paper shows that built-in handling of missing values in LSTM network training paves the way for application of RNNs in disease progression modeling.
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
A median target registration error of 3. 6 mm on landmark centroids and a median Dice of 0. 87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
Here, we overcome this limitation with a probabilistic method that simultaneously solves for registration and synthesis directly on the target images, without any training data.
Label-driven weakly-supervised learning for multimodal deformable image registration
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms.
NiftyNet: a deep-learning platform for medical imaging
NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.
Part-to-whole Registration of Histology and MRI using Shape Elements
We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology.
Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion
The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy.
